Sorry, I might not have been clear about what I meant by "bottom-heavy". I think we actually agree - as someone who's hiring for DS roles right now, I've seen a ton of exactly what you're talking about.

-Some candidates can write great code, but don't have the math background to understand what ML black boxes are doing.

-Then there are STEM PhDs that have never written non-research (i.e. maintainable) code or had to formulate a qualitative business problem into a quantitative problem they can solve.

Both types of candidates need to come in at a "junior" level and do some on-the-job learning in order to be fully successful data scientists. IMO it appears to be easier to teach STEM PhDs how to code than programmers how to do math, but that might be personal bias (since I came from the former group).

Wonder if the finance roles of quants and quant devs will spread to other industries. Quant devs are math heavy programmers that might not do original research but still can understand/calibrate/implement the models the pure quants produce. Ie given an abstract paper with a shiny model (or a hacked together spreadsheet...) the qdev might need to analyze what monte Carlo error correction strategies are relevant for the problem or how a certain market's peculiarities might influence calibrations etc.

Also, quant devs are heavily involved in building the calculation engines that invokes the models. These engines handles real-time dataflow and calibrations etc and are often highly non-trivial.

My guess is that that type of role is relevant in a data science context. This is much more than data cleansing and piping data between databases.

Heck, when I was in school of CS degree, some people from literature undergraduate went straight to CS graduate programs without too much a pain.

Tuned out programming never really required much math background, it is the level brain teaser that programming posed is as much as math education. So anyone who's has survived math advanced degree would take program like piece of cake, but it doesn't mean people from non-STEM background is hopeless to master data science.

Yet it's a joke to refer data science without referencing to advanced math concept. Albeit significant domain knowledge, data science is not just business analysis aided with spreadsheet. Modelling is an essential part of.

> Both types of candidates need to come in at a "junior" level

Then what's the point of the Ph.D.? Why not just go straight from B.S. to junior data scientist then?

In theory, any programmer worth their salt would already know a massive amount of math (comparatively) and should be readily capable of learning more. If you program without a solid understanding of the underlying math, you're not programming. You're typing until it compiles.

Disagree. As someone who knows more mathematics and less programming than the average programmer I'd say the average programmer need not know all that much mathematics at all, if they're not working in a particular area that involves mathematics.

You must already know vector math or be capable of learning it in less than a day. If you don't have that aptitude, then I put you at higher in the stack.

Vectors aren't terribly advanced mathematics.

You're joking, right? What math, algebra 2 math?

I come from a quantitative social sciences background. I won't math quite as good as your average STEM PhD, but I like to think those of us in social sciences do a pretty good job of building good questions to parameterize squishy qualitative business objectives.